Automated adaptive preconditioner for quantitative susceptibility mapping

Robust breast quantitative susceptibility mapping in the presence of silicone

PURPOSE

To (a) develop a preconditioned water-fat-silicone total field inversion (wfsTFI) algorithm that directly estimates the susceptibility map from complex multi-echo data in the breast in the presence of silicone and to (b) evaluate the performance of wfsTFI for breast quantitative susceptibility mapping (QSM) in silico and in vivo in comparison with formerly proposed methods.

METHODS

Numerical simulations and in vivo multi-echo gradient echo breast measurements were performed to compare wfsTFI to a previously proposed field map-based linear total field inversion algorithm (lTFI) with and without the consideration of the chemical shift of silicone in the field map estimation step. Specifically, a simulation based on an in vivo scan and data from five patients were included in the analysis.

RESULTS

In the simulation, wfsTFI is able to significantly decrease the normalized root mean square error from lTFI without (4.46) and with (1.77) the consideration of the chemical shift of silicone to 0.68. Both the in silico and in vivo wfsTFI susceptibility maps show reduced shadowing artifacts in local tissue adjacent to silicone, reduced streaking artifacts and no erroneous single voxels of diamagnetic susceptibility in proximity to silicone.

CONCLUSION

The proposed wfsTFI method can automatically distinguish between subjects with and without silicone. Furthermore wfsTFI accounts for the presence of silicone in the QSM dipole inversion and allows for the robust estimation of susceptibility in proximity to silicone breast implants and hence allows the visualization of structures that would otherwise be dominated by artifacts on susceptibility maps.

Susceptibility artifact correction in MR thermometry for monitoring of mild radiofrequency hyperthermia using total field inversion

PURPOSE

MR temperature monitoring of mild radiofrequency hyperthermia (RF-HT) of cancer exploits the linear resonance frequency shift of water with temperature. Motion-induced susceptibility distribution changes cause artifacts that we correct here using the total field inversion (TFI) approach.

METHODS

The performance of TFI was compared to two background field removal (BFR) methods: Laplacian boundary value (LBV) and projection onto dipole fields (PDF). Data sets with spatial susceptibility change and B 0 -drift were simulated, phantom heating experiments were performed, four volunteer data sets at thermoneutral conditions as well as data from one cervical cancer, two sarcoma, and one seroma patients undergoing mild RF-HT were corrected using the proposed methods.

RESULTS

Simulations and phantom heating experiments revealed that using BFR or TFI preserves temperature-induced phase change, while removing susceptibility artifacts and B 0 -drift. TFI resulted in the least cumulative error for all four volunteers. Temperature probe information from four patient data sets were best depicted by TFI-corrected data in terms of accuracy and precision. TFI also performed best in case of the sarcoma treatment without temperature probe.

CONCLUSION

TFI outperforms previously suggested BFR methods in terms of accuracy and robustness. While PDF consistently overestimates susceptibility contribution, and LBV removes valuable pixel information, TFI is more robust and leads to more accurate temperature estimations.

Streaking artifact suppression of quantitative susceptibility mapping reconstructions via L1-norm data fidelity optimization (L1-QSM)

PURPOSE

The presence of dipole-inconsistent data due to substantial noise or artifacts causes streaking artifacts in quantitative susceptibility mapping (QSM) reconstructions. Often used Bayesian approaches rely on regularizers, which in turn yield reduced sharpness. To overcome this problem, we present a novel L1-norm data fidelity approach that is robust with respect to outliers, and therefore prevents streaking artifacts.

METHODS

QSM functionals are solved with linear and nonlinear L1-norm data fidelity terms using functional augmentation, and are compared with equivalent L2-norm methods. Algorithms were tested on synthetic data, with phase inconsistencies added to mimic lesions, QSM Challenge 2.0 data, and in vivo brain images with hemorrhages.

RESULTS

The nonlinear L1-norm-based approach achieved the best overall error metric scores and better streaking artifact suppression. Notably, L1-norm methods could reconstruct QSM images without using a brain mask, with similar regularization weights for different data fidelity weighting or masking setups.

CONCLUSION

The proposed L1-approach provides a robust method to prevent streaking artifacts generated by dipole-inconsistent data, renders brain mask calculation unessential, and opens novel challenging clinical applications such asassessing brain hemorrhages and cortical layers.

Preconditioned water-fat total field inversion: Application to spine quantitative susceptibility mapping

PURPOSE

To (a) develop a preconditioned water-fat total field inversion (wfTFI) algorithm that directly estimates the susceptibility map from complex multi-echo gradient echo data for water-fat regions and to (b) evaluate the performance of the proposed wfTFI quantitative susceptibility mapping (QSM) method in comparison with a local field inversion (LFI) method and a linear total field inversion (TFI) method in the spine.

METHODS

Numerical simulations and in vivo spine multi-echo gradient echo measurements were performed to compare wfTFI to an algorithm based on disjoint background field removal (BFR) and LFI and to a formerly proposed TFI algorithm. The data from 1 healthy volunteer and 10 patients with metastatic bone disease were included in the analysis. Clinical routine computed tomography (CT) images were used as a reference standard to distinguish osteoblastic from osteolytic changes. The ability of the QSM methods to distinguish osteoblastic from osteolytic changes was evaluated.

RESULTS

The proposed wfTFI method was able to decrease the normalized root mean square error compared to the LFI and TFI methods in the simulation. The in vivo wfTFI susceptibility maps showed reduced BFR artifacts, noise amplification, and streaking artifacts compared to the LFI and TFI maps. wfTFI provided a significantly higher diagnostic confidence in differentiating osteolytic and osteoblastic lesions in the spine compared to the LFI method (p = .012).

CONCLUSION

The proposed wfTFI method can minimize BFR artifacts, noise amplification, and streaking artifacts in water-fat regions and can thus better differentiate between osteoblastic and osteolytic changes in patients with metastatic disease compared to LFI and the original TFI method.

Single-step calculation of susceptibility through multiple orientation sampling

Quantitative susceptibility mapping (QSM) was developed to estimate the spatial distribution of magnetic susceptibility from MR signal phase acquired using a gradient echo (GRE) sequence. The field-to-susceptibility inversion in QSM is known to be ill-posed and needs numerical stabilization through either regularization or data oversampling. The calculation of susceptibility through the multiple orientation sampling (COSMOS) method uses phase data acquired at three or more head orientations to achieve a well-conditioned field-to-susceptibility inversion and is often considered the gold standard for in vivo QSM. However, the conventional COSMOS approach, here named multistep COSMOS (MSCOSMOS), solves the dipole inversion from the local field derived from raw GRE phase through multiple steps of phase preprocessing. Error propagations between these consecutive phase processing steps can thus affect the final susceptibility quantification. On the other hand, recently proposed single-step QSM (SSQSM) methods aim to solve an integrated inversion from unprocessed or total phase to mitigate such error propagations but have been limited to single orientation QSM. This study therefore aimed to test the feasibility of using single-step COSMOS (SSCOSMOS) to jointly perform background field removal and dipole inversion with multiple orientation sampling, which could serve as a better standard for gauging SSQSM methods. We incorporated multiple spherical mean value (SMV) kernels of various radii with the dipole inversion in SSCOSMOS. QSM reconstructions with SSCOSMOS and MSCOSMOS were compared using both simulations with a numerical head phantom and in vivo human brain data. SSCOSMOS permitted integrated background removal and dipole inversion without the need to adjust any regularization parameters. In addition, with sufficiently large SMV kernels, SSCOSMOS performed consistently better than MSCOSMOS in all the tested error metrics in our simulations, giving better susceptibility quantification and smaller reconstruction error. Consistent tissue susceptibility values were obtained between SSCOSMOS and MSCOSMOS.

Multiecho complex total field inversion method (mcTFI) for improved signal modeling in quantitative susceptibility mapping

PURPOSE

Typical quantitative susceptibility mapping (QSM) reconstruction steps consist of first estimating the magnetization field from the gradient-echo images, and then reconstructing the susceptibility map from the estimated field. The errors from the field-estimation steps may propagate into the final QSM map, and the noise in the estimated field map may no longer be zero-mean Gaussian noise, thus, causing streaking artifacts in the resulting QSM. A multiecho complex total field inversion (mcTFI) method was developed to compute the susceptibility map directly from the multiecho gradient echo images using an improved signal model that retains the Gaussian noise property in the complex domain. It showed improvements in QSM reconstruction over the conventional field-to-source inversion.

METHODS

The proposed mcTFI method was compared with the nonlinear total field inversion (nTFI) method in a numerical brain with hemorrhage and calcification, the numerical brains provided by the QSM Challenge 2.0, 18 brains with intracerebral hemorrhage scanned at 3T, and 6 healthy brains scanned at 7T.

RESULTS

Compared with nTFI, the proposed mcTFI showed more accurate QSM reconstruction around the lesions in the numerical simulations. The mcTFI reconstructed QSM also showed the best image quality with the least artifacts in the brains with intracerebral hemorrhage scanned at 3T and healthy brains scanned at 7T.

CONCLUSION

The proposed multiecho complex total field inversion improved QSM reconstruction over traditional field-to-source inversion through better signal modeling.

Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping

Adaptive Total Field Inversion is described for quantitative susceptibility mapping (QSM) reconstruction from total field data through a spatially adaptive suppression of shadow artifacts through spatially adaptive regularization. The regularization for shadow suppression consists of penalizing low-frequency components of susceptibility in regions of small susceptibility contrasts as estimated by R2∗ derived signal intensity. Compared with a conventional local field method and two previously proposed regularized total field inversion methods, improvements were demonstrated in phantoms and subjects without and with hemorrhages. This algorithm, named TFIR, demonstrates the lowest error in numerical and gadolinium phantom datasets. In COSMOS data, TFIR performs well in matching ground truth in high-susceptibility regions. For patient data, TFIR comes close to meeting the quality of the reference local field method and outperforms other total field techniques in both clinical scores and shadow reduction.

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TFIR's adaptive regularization obtains magnetic susceptibility from magnetic field

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TFIR has low artifact incidence on both quantitative and clinical scores

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The error for TFIR is low on various numerical and ground truth tests

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Clinical applications for TFIR include hemorrhages and whole head mapping